Today, we are delighted to announce that DeepSeek R1 distilled Llama and Qwen designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek AI’s first-generation frontier design, DeepSeek-R1, along with the distilled versions ranging from 1.5 to 70 billion criteria to develop, experiment, and responsibly scale your generative AI ideas on AWS.

In this post, we demonstrate how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps to deploy the distilled versions of the designs too.

Overview of DeepSeek-R1

DeepSeek-R1 is a large language model (LLM) developed by DeepSeek AI that uses support discovering to improve reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base foundation. An essential distinguishing function is its support knowing (RL) step, which was used to refine the design’s reactions beyond the basic pre-training and tweak procedure. By integrating RL, DeepSeek-R1 can adapt better to user feedback and objectives, eventually improving both relevance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, it’s geared up to break down complicated inquiries and factor through them in a detailed manner. This guided reasoning process enables the model to produce more accurate, transparent, and detailed answers. This model integrates RL-based fine-tuning with CoT capabilities, aiming to produce structured responses while focusing on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has actually caught the market’s attention as a versatile text-generation model that can be integrated into numerous workflows such as representatives, sensible thinking and information analysis tasks.

DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture enables activation of 37 billion specifications, enabling efficient inference by routing inquiries to the most pertinent expert “clusters.” This approach allows the design to specialize in various problem domains while maintaining total performance. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge instance to release the model. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.

DeepSeek-R1 distilled models bring the thinking capabilities of the main R1 model to more effective architectures based on popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of training smaller sized, more effective models to mimic the behavior and reasoning patterns of the bigger DeepSeek-R1 model, utilizing it as a teacher design.

You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we advise releasing this design with guardrails in location. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, avoid hazardous material, and evaluate designs against essential safety requirements. At the time of writing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce numerous guardrails tailored to various use cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing security controls across your generative AI applications.

Prerequisites

To release the DeepSeek-R1 design, you require access to an ml.p5e circumstances. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and verify you’re utilizing ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are releasing. To ask for a limitation increase, develop a limit boost request and connect to your account team.

Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) authorizations to use Amazon Bedrock Guardrails. For guidelines, see Establish consents to use guardrails for material filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails allows you to present safeguards, prevent hazardous material, and examine designs against key safety requirements. You can implement safety measures for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to evaluate user inputs and design responses deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a guardrail utilizing the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo.

The general circulation involves the following steps: First, the system receives an input for the design. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it’s sent out to the model for reasoning. After receiving the model’s output, another guardrail check is applied. If the output passes this final check, it’s returned as the final result. However, if either the input or output is stepped in by the guardrail, a message is returned indicating the nature of the intervention and whether it happened at the input or output stage. The examples showcased in the following sections show inference utilizing this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

Amazon Bedrock Marketplace gives you access to over 100 popular, emerging, and specialized structure models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following steps:

1. On the Amazon Bedrock console, choose Model catalog under Foundation models in the navigation pane. At the time of writing this post, you can utilize the InvokeModel API to invoke the model. It doesn’t support Converse APIs and other Amazon Bedrock tooling.

1. Filter for DeepSeek as a service provider and choose the DeepSeek-R1 design.
   
   The design detail page provides important details about the design’s abilities, pricing structure, and implementation standards. You can find detailed use directions, including sample API calls and code snippets for combination. The design supports various text generation jobs, including content development, code generation, and question answering, using its support discovering optimization and CoT thinking capabilities. The page also consists of release choices and licensing details to help you get going with DeepSeek-R1 in your applications.
2. To start using DeepSeek-R1, select Deploy.
   
   You will be prompted to set up the implementation details for DeepSeek-R1. The design ID will be pre-populated.
3. For Endpoint name, enter an endpoint name (between 1-50 alphanumeric characters).
4. For Variety of circumstances, get in a variety of instances (between 1-100).
5. For example type, select your instance type. For ideal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested. Optionally, you can set up advanced security and infrastructure settings, consisting of virtual personal cloud (VPC) networking, service role consents, and encryption settings. For the majority of utilize cases, the default settings will work well. However, for production deployments, you might wish to review these settings to align with your organization’s security and compliance requirements.
6. Choose Deploy to start utilizing the model.
   
   When the deployment is complete, you can check DeepSeek-R1’s abilities straight in the Amazon Bedrock play ground.
7. Choose Open in play ground to access an interactive user interface where you can explore different triggers and change model specifications like temperature and maximum length. When utilizing R1 with Bedrock’s InvokeModel and Playground Console, use DeepSeek’s chat design template for optimal outcomes. For example, material for inference.
   
   This is an exceptional way to explore the design’s thinking and text generation abilities before integrating it into your applications. The play ground provides instant feedback, assisting you understand how the model reacts to numerous inputs and letting you fine-tune your triggers for optimal results.
   
   You can rapidly check the model in the playground through the UI. However, to invoke the released model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
   
   Run reasoning utilizing guardrails with the deployed DeepSeek-R1 endpoint
   
   The following code example shows how to carry out inference using a released DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can produce a guardrail utilizing the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo. After you have created the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime client, configures reasoning criteria, and sends a request to create text based upon a user prompt.
   
   Deploy DeepSeek-R1 with SageMaker JumpStart
   
   SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, integrated algorithms, and prebuilt ML solutions that you can release with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your information, and deploy them into production using either the UI or SDK.
   
   Deploying DeepSeek-R1 design through SageMaker JumpStart offers 2 convenient approaches: utilizing the intuitive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let’s explore both methods to assist you choose the method that best suits your needs.
   
   Deploy DeepSeek-R1 through SageMaker JumpStart UI
   
   Complete the following steps to deploy DeepSeek-R1 using SageMaker JumpStart:
   
   1. On the SageMaker console, choose Studio in the navigation pane.
8. First-time users will be prompted to produce a domain.
9. On the SageMaker Studio console, pick JumpStart in the navigation pane.
   
   The design internet browser shows available models, with details like the company name and model capabilities.
   
   4. Look for DeepSeek-R1 to view the DeepSeek-R1 model card. Each design card reveals crucial details, consisting of:
   
   - Model name - Provider name
10. Task category (for example, Text Generation). Bedrock Ready badge (if relevant), indicating that this design can be signed up with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to conjure up the model
    
    5. Choose the design card to see the design details page.
    
    The model details page includes the following details:
    
    - The model name and provider details. Deploy button to deploy the model. About and Notebooks tabs with detailed details
    
    The About tab consists of essential details, such as:
    
    - Model description.
11. License details.
12. Technical specifications.
13. Usage standards
    
    Before you deploy the model, it’s recommended to examine the design details and license terms to confirm compatibility with your use case.
    
    6. Choose Deploy to proceed with release.
    
    7. For Endpoint name, use the immediately created name or create a customized one.
14. For example type ¸ choose an instance type (default: ml.p5e.48 xlarge).
15. For Initial instance count, go into the variety of circumstances (default: 1). Selecting proper circumstances types and counts is vital for expense and efficiency optimization. Monitor your implementation to change these settings as needed.Under Inference type, Real-time inference is chosen by default. This is optimized for sustained traffic and low latency.
16. Review all setups for precision. For this design, we highly suggest sticking to SageMaker JumpStart default settings and making certain that network isolation remains in place.
17. Choose Deploy to deploy the model.
    
    The release process can take numerous minutes to finish.
    
    When release is complete, your endpoint status will change to InService. At this point, the model is ready to accept inference demands through the endpoint. You can keep an eye on the implementation development on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the release is complete, you can conjure up the design using a SageMaker runtime customer and incorporate it with your applications.
    
    Deploy DeepSeek-R1 using the SageMaker Python SDK
    
    To begin with DeepSeek-R1 using the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the essential AWS approvals and archmageriseswiki.com environment setup. The following is a detailed code example that demonstrates how to release and use DeepSeek-R1 for reasoning programmatically. The code for deploying the design is offered in the Github here. You can clone the notebook and range from SageMaker Studio.
    
    You can run extra requests against the predictor:
    
    Implement guardrails and run reasoning with your SageMaker JumpStart predictor
    
    Similar to Amazon Bedrock, you can also use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail using the Amazon Bedrock console or the API, and execute it as displayed in the following code:
    
    Clean up
    
    To prevent undesirable charges, complete the actions in this section to tidy up your resources.
    
    Delete the Amazon Bedrock Marketplace deployment
    
    If you deployed the design using Amazon Bedrock Marketplace, total the following steps:
    
    1. On the Amazon Bedrock console, under Foundation models in the navigation pane, pick Marketplace releases.
18. In the Managed releases section, find the endpoint you want to delete.
19. Select the endpoint, and on the Actions menu, select Delete.
20. Verify the endpoint details to make certain you’re erasing the appropriate deployment: 1. Endpoint name.
21. Model name.
22. Endpoint status
    
    Delete the SageMaker JumpStart predictor
    
    The SageMaker JumpStart design you released will sustain costs if you leave it running. Use the following code to delete the endpoint if you desire to stop sustaining charges. For more details, see Delete Endpoints and Resources.
    
    Conclusion
    
    In this post, we checked out how you can access and deploy the DeepSeek-R1 model utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to begin. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting going with Amazon SageMaker JumpStart.
    
    About the Authors
    
    Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He assists emerging generative AI business develop ingenious options using AWS services and accelerated compute. Currently, he is focused on developing techniques for fine-tuning and optimizing the reasoning performance of large language designs. In his totally free time, Vivek enjoys hiking, watching films, and trying various cuisines.
    
    Niithiyn Vijeaswaran is a Generative AI Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS AI accelerators (AWS Neuron). He holds a Bachelor’s degree in Computer Science and Bioinformatics.
    
    Jonathan Evans is a Specialist Solutions Architect dealing with generative AI with the Third-Party Model Science team at AWS.
    
    Banu Nagasundaram leads item, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker’s artificial intelligence and generative AI center. She is passionate about developing solutions that assist consumers accelerate their AI journey and unlock business worth.